class_docstr: Inference Dataset to perform prediction.
  
  Args:
      path (str | Path): Path to an image or image-folder.
      transform (A.Compose | None, optional): Albumentations Compose object describing the transforms that are
          applied to the inputs.
      image_size (int | tuple[int, int] | None, optional): Target image size
          to resize the original image. Defaults to None.
docstr: Get the number of images in the given path.
docstr_tok: ["Get","the","number","of","images","in","the","given","path","."]
code: def __len__(self) -> int:
      """Get the number of images in the given path."""
      return len(self.image_filenames)
  
file_path: anomalib/src/anomalib/data/inference.py
imports: from __future__ import annotations
  from pathlib import Path
  from typing import Any
  import albumentations
  from torch.utils.data.dataset import Dataset
  from anomalib.data.utils import get_image_filenames, get_transforms, read_image
label_desc: Use image node_id 2 for calling the InferenceDataset obj's underlying member method code with example usage: obj.__len__() and returns: len